998211c483
- 从服务器拉取完整代码 - 按框架规范整理项目结构 - 配置 Drone CI 测试环境部署 - 包含后端(FastAPI)、前端(Vue3)、管理端 技术栈: Vue3 + TypeScript + FastAPI + MySQL
140 lines
5.3 KiB
Python
140 lines
5.3 KiB
Python
"""
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系统API - 供外部服务回调使用
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"""
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import logging
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from typing import List, Dict, Any, Optional
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from fastapi import APIRouter, Depends, HTTPException, status, Header
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from sqlalchemy.ext.asyncio import AsyncSession
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from pydantic import BaseModel, Field
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from app.core.deps import get_db
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from app.schemas.base import ResponseModel
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from app.schemas.course import KnowledgePointCreate
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from app.services.course_service import knowledge_point_service, course_service
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logger = logging.getLogger(__name__)
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router = APIRouter(prefix="/system")
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class KnowledgePointData(BaseModel):
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"""知识点数据模型"""
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name: str = Field(..., description="知识点名称")
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description: str = Field(default="", description="知识点描述")
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type: str = Field(default="理论知识", description="知识点类型")
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source: int = Field(default=1, description="来源：0=手动，1=AI分析")
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topic_relation: Optional[str] = Field(None, description="与主题的关系描述")
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class KnowledgeCallbackRequest(BaseModel):
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"""知识点回调请求模型（已弃用，保留向后兼容）"""
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course_id: int = Field(..., description="课程ID")
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material_id: int = Field(..., description="资料ID")
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knowledge_points: List[KnowledgePointData] = Field(..., description="知识点列表")
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@router.post("/knowledge", response_model=ResponseModel[Dict[str, Any]])
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async def create_knowledge_points_callback(
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request: KnowledgeCallbackRequest,
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authorization: str = Header(None),
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db: AsyncSession = Depends(get_db),
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):
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"""
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创建知识点回调接口（已弃用）
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注意：此接口已弃用，知识点分析现使用 Python 原生实现。
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保留此接口仅为向后兼容。
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"""
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try:
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# API密钥验证（已弃用的接口，保留向后兼容）
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expected_token = "Bearer callback-token-2025"
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if authorization != expected_token:
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raise HTTPException(
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status_code=status.HTTP_401_UNAUTHORIZED,
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detail="无效的授权令牌"
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)
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# 验证课程是否存在
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course = await course_service.get_by_id(db, request.course_id)
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if not course:
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raise HTTPException(
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status_code=status.HTTP_404_NOT_FOUND,
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detail=f"课程 {request.course_id} 不存在"
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)
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# 验证资料是否存在
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materials = await course_service.get_course_materials(db, course_id=request.course_id)
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material = next((m for m in materials if m.id == request.material_id), None)
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if not material:
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raise HTTPException(
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status_code=status.HTTP_404_NOT_FOUND,
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detail=f"资料 {request.material_id} 不存在"
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)
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# 创建知识点
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created_points = []
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for kp_data in request.knowledge_points:
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try:
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knowledge_point_create = KnowledgePointCreate(
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name=kp_data.name,
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description=kp_data.description,
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type=kp_data.type,
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source=kp_data.source, # AI分析来源=1
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topic_relation=kp_data.topic_relation,
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material_id=request.material_id # 关联资料ID
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)
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# 使用系统用户ID (假设为1，或者可以配置)
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system_user_id = 1
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knowledge_point = await knowledge_point_service.create_knowledge_point(
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db=db,
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course_id=request.course_id,
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point_in=knowledge_point_create,
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created_by=system_user_id
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)
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created_points.append({
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"id": knowledge_point.id,
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"name": knowledge_point.name,
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"description": knowledge_point.description,
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"type": knowledge_point.type,
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"source": knowledge_point.source,
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"material_id": knowledge_point.material_id
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})
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except Exception as e:
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logger.error(
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f"创建知识点失败 - name: {kp_data.name}, error: {str(e)}"
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)
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# 继续处理其他知识点，不因为单个失败而中断
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continue
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logger.info(
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f"知识点回调成功 - course_id: {request.course_id}, material_id: {request.material_id}, created_points: {len(created_points)}"
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)
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return ResponseModel(
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data={
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"course_id": request.course_id,
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"material_id": request.material_id,
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"knowledge_points_count": len(created_points),
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"knowledge_points": created_points
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},
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message=f"成功创建 {len(created_points)} 个知识点"
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)
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except HTTPException:
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raise
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except Exception as e:
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logger.error(
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f"知识点回调处理失败 - course_id: {request.course_id}, material_id: {request.material_id}, error: {str(e)}",
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exc_info=True
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)
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raise HTTPException(
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status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
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detail="知识点创建失败"
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)
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